Evidence map›Paper›PMID 40806562›Full record

ArticleInternational journal of molecular sciences2025

A Natural Language Processing Method Identifies an Association Between Bacterial Communities in the Upper Genital Tract and Ovarian Cancer.

Andrew Polio, Vincent Wagner, David P Bender, Michael J Goodheart, Jesus Gonzalez Bosquet

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Andrew PolioDepartment of Obstetrics and Gynecology, University of Iowa, 200 Hawkins dr., Iowa City, IA 52242, USA.
Vincent WagnerDepartment of Obstetrics and Gynecology, University of Iowa, 200 Hawkins dr., Iowa City, IA 52242, USA.ORCID 0000-0002-0402-3483
David P BenderDepartment of Obstetrics and Gynecology, University of Iowa, 200 Hawkins dr., Iowa City, IA 52242, USA.
Michael J GoodheartDepartment of Obstetrics and Gynecology, University of Iowa, 200 Hawkins dr., Iowa City, IA 52242, USA.
Jesus Gonzalez BosquetDepartment of Obstetrics and Gynecology, University of Iowa, 200 Hawkins dr., Iowa City, IA 52242, USA.ORCID 0000-0002-2079-4528

Funding

NIH HHS NIH 5R01CA99908-18
6 · The paper itself

Abstract

Bacterial communities within the female upper genital tract may influence the risk of ovarian cancer. In this retrospective cohort pilot study, we aim to detect different communities of bacteria between ovarian cancer and normal controls using topic modeling, a natural language processing tool. RNA was extracted and analyzed using the VITCOMIC2 pipeline. Topic modeling assessed differences in bacterial communities. Idatuning identified an optimal latent topic number and Latent Dirichlet Allocation (LDA) assessed topic differences between high-grade serous ovarian cancer (HGSOC) and controls. Results were validated using The Cancer Genome Atlas (TCGA) HGSOC dataset. A total of 801 unique taxa were identified, with 13 bacteria significantly differing between HGSOC and normal controls. LDA modeling revealed a latent topic associated with HGSOC samples, containing bacteria

Indexed as

BacteriaGenitalia, FemaleMicrobiotaNatural Language ProcessingOvarian NeoplasmsFemaleHumansMiddle AgedPilot ProjectsRetrospective Studiesmicrobiomenatural language processingovarian cancerprediction modelRNAseqRNA sequencing

Identifiers

PMID40806562
PMCPMC12347966

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.